Research Papers

The Influence of Interaction Technology on the Learning of Assembly Tasks Using Virtual Reality

[+] Author and Article Information
Yaiza Vélaz

Department of Mechanical Engineering,
University of Navarra, Manuel Lardizabal 15,
San Sebastián 20018, Spain
e-mail: yvelaz@gmail.com

Jorge Rodríguez Arce

Department of Mechanical Engineering,
University of Navarra,
Manuel Lardizabal 15,
San Sebastián 20018, Spain
e-mail: jorge.arce.mx@gmail.com

Teresa Gutiérrez

Industry and Transport Division,
C/Geldo—Parque Tecnologico de Bizkaia,
Edificio 700,
Derio 48160, Spain;
San Sebastián 20009, Spain
e-mail: teresa.gutierrez@tecnalia.com

Alberto Lozano-Rodero

Department of Mechanical Engineering,
University of Navarra,
Manuel Lardizabal 15,
San Sebastián 20018, Spain
e-mail: alozanorodero@gmail.com

Angel Suescun

Department of Mechanical Engineering,
University of Navarra,
Manuel Lardizabal 15,
San Sebastián 20018, Spain
e-mail: asuescun@ceit.es

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received July 25, 2012; final manuscript received September 7, 2014; published online October 7, 2014. Editor: Bahram Ravani.

J. Comput. Inf. Sci. Eng 14(4), 041007 (Oct 07, 2014) (9 pages) Paper No: JCISE-12-1121; doi: 10.1115/1.4028588 History: Received July 25, 2012; Revised September 07, 2014

This paper focuses on the use of virtual reality (VR) systems for teaching industrial assembly tasks and studies the influence of the interaction technology on the learning process. The experiment conducted follows a between-subjects design with 60 participants distributed in five groups. Four groups were trained on the target assembly task with a VR system, but each group used a different interaction technology: mouse-based, Phantom Omni® haptic, and two configurations of the Markerless Motion Capture (Mmocap) system (with 2D or 3D tracking of hands). The fifth group was trained with a video tutorial. A post-training test carried out the day after evaluated performance in the real task. The experiment studies the efficiency and effectiveness of each interaction technology for learning the task, taking in consideration both quantitative measures (such as training time, real task performance, evolution from the virtual task to real one), and qualitative data (user feedback from a questionnaire). Results show that there were no significant differences in the final performance among the five groups. However, users trained under mouse and 2D-tracking Mmocap systems took significantly less training time than the rest of the virtual modalities. This brings out two main outcomes: (1) the perception of collisions using haptics does not increase the learning transfer of procedural tasks demanding low motor skills and (2) Mmocap-based interactions can be valid for training this kind of tasks.

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Fig. 1

The IMA system. Left: using the LHIFAM haptic device to interact with the virtual objects and perform the assembly task. Middle: indirect aids provide information about the current subtask. Right: direct aids (in addition to indirect aids) provide information about the next action.

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Fig. 2

The five subtasks of the experimental assembly task

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Fig. 3

The post-training test: participants had to assemble the real valve on their own

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Fig. 4

Boxplot of training times in session 1 (in seconds)

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Fig. 5

Boxplot of training times in the session 2 (in seconds)

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Fig. 6

Boxplot of the times to assemble the real valve in seconds

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Fig. 7

Boxplot of the performance indicators (% of correct task) for each training condition

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Fig. 8

Typology of errors made in each group. Numbers inside the vertical bars represent the number of errors done in each category.

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Fig. 9

Depiction of performance evolution from the virtual task to the real task for each interaction condition. Results are presented as percentage of correct steps without any aid or error for each participant (vertical bar).

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Fig. 10

Median and quartiles values for each usability question. Box corresponds to 95% confidence interval. Q1–Q9 correspond to the usability questionnaire described in Experiment Design section.



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